The network is trained for 300k iterations using the Adam optimizer [1]} with a learning rate of 0.002 exponential decayed at a rate of 0.99 every 150 iterations. We use a batch size of 128 distributed over 32 GPUs. For vehicles, we learn a multimodal distribution with three components (\(K=3\) ), and for pedestrians and bikes, we learn a unimodal distribution (\(K=1\) ).
